Thermal conductivity affects every stage of additive manufacturing: from heat transfer during melting/solidification to the thermal performance of final parts. ML provides fast surrogate models for computationally expensive thermal simulations and enables inverse design of materials with target thermal properties.

Melt Pool Thermal Modeling

The melt pool in metal AM experiences extreme thermal gradients (10^6 K/s cooling rates). ML surrogates predict melt pool geometry and temperature fields.

• Heat source models: Gaussian, double-ellipsoid adapted by ML
• Temperature field prediction: CNN surrogates for FEM solutions
• Cooling rate estimation: Critical for microstructure
• Thermal history tracking: LSTM for time-dependent fields

Residual Stress Prediction

Thermal gradients cause residual stresses that can lead to distortion and cracking. ML predicts stress distributions from thermal history.

Prediction Target	ML Method	Input Data	Error
Melt pool dimensions	Neural Network	Power, speed, k	< 5%
Temperature field	CNN / U-Net	Geometry, parameters	< 3%
Residual stress	PINN	Thermal history	< 8%
Distortion	GNN	Part geometry, scan path	< 10%

Physics-Informed Neural Networks (PINNs)

PINNs embed heat transfer equations directly into neural network loss functions, ensuring physically consistent predictions even with limited data.

• Conservation laws: Energy balance enforced in loss
• Boundary conditions: Convection, radiation at surfaces
• Material properties: Temperature-dependent k, Cp

Effective Thermal Conductivity

3D printed parts often have different thermal conductivity than bulk materials due to porosity, anisotropy, and microstructure variations.

Material	Bulk k (W/mK)	AM k (W/mK)	ML Prediction R2
Ti-6Al-4V	6.7	5.5-7.0	0.92
Inconel 718	11.4	9-12	0.89
AlSi10Mg	130	100-140	0.94
316L SS	16.3	13-17	0.91
Copper alloys	400	250-380	0.87

Anisotropy Effects

Layer-by-layer fabrication creates directional differences in thermal conductivity.

• Build direction: Typically 10-20% lower k
• Scan strategy: Affects grain orientation and k anisotropy
• ML modeling: Tensor predictions for directional k

High Thermal Conductivity Materials

ML guides development of AM-compatible materials for thermal management applications.

• Copper alloys: CuCrZr for high k while maintaining printability
• Aluminum matrix composites: Al + diamond/SiC particles
• Lattice structures: Topology optimization for heat dissipation

Thermal Barrier Materials

• Ceramic coatings: YSZ, rare-earth zirconates
• Porous structures: ML-optimized porosity for insulation
• Multi-material printing: Gradients for thermal management

Inverse Design

Given target thermal properties, ML identifies optimal compositions and structures.

• Composition optimization: Bayesian optimization for alloy design
• Topology optimization: Lattice design for specific k
• Multifunctional design: Balance k with strength, weight

Heat Exchangers

AM enables complex internal channels impossible with conventional manufacturing.

• Conformal cooling: Injection molds with optimized channels
• Microchannel heat sinks: Electronics thermal management
• TPMS structures: Triply periodic minimal surfaces for heat transfer

Aerospace Thermal Management

• Turbine components: Thermal barrier coatings
• Rocket nozzles: Regenerative cooling channels
• Heat shields: Ablative material gradients

Electronics Cooling

• LED heat sinks: Optimized fin geometries
• Power electronics: High-k substrates
• Battery thermal management: EV applications

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